Bloodstream infections (BSIs) have risen to become the 6th leading cause of death in the U.S. and the most expensive hospital-treated condition, at over $30B annually. BSIs account for 25% of all ICU usage and roughly 50% of all hospital deaths in the U.S. BSIs are typically caused by bacteria or fungi, and effective disease management requires their early and accurate identification. BSIs are typically identified through a series of blood-cultures that take up to several days to identify potential pathogens. Blood-cultures are widely considered the barrier to a hypothesis driven first-line antimicrobial intervention.
Modern molecular approaches have the potential to revolutionize this field, however limitations including lack of sensitivity, inaccurate performance, narrow coverage, and insufficient diagnostic detail have prevented these methods from making an impact. Indeed, in contrast to numerous infectious diseases, a clear capability gap remains despite the immense clinical need. It is the combined difficulty of immensely low pathogen loads (1-100 CFU/ml), the requirement for broad coverage with high levels of detail (20 pathogens are responsible for roughly 90% of cases where species level information is clinically required), a difficult specimen matrix (blood), and the need for a rapid turn-around; all of which when combined, have proven difficult to overcome.
Molecular diagnostic methods for identifying microbial pathogens can be performed by probing for conserved regions in their respective genomic material. Methods for genomic identification include isolation and detection of pathogenic DNA. It is further advantageous to develop an automated manner in which to conduct molecular processes.
Automated molecular processes have the advantage of being less likely to be compromised due to human error, contaminations, and are potentially faster. Furthermore, they hold the potential to provide more repeatable results; a highly sought after trait.